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Course Catalog

140.677.11 Biostatistical Analysis of Epidemiologic Data II: Poisson and Conditional Logistic Regression Analysis

Department:
Biostatistics
Term:
Summer Inst. term
Credits:
2 credits
Academic Year:
2017 - 2018
Location:
East Baltimore
Dates:
Mon 06/19/2017 - Fri 06/23/2017
Class Times:
  • M Tu W Th F,  8:30am - 12:00pm
Auditors Allowed:
No
Grading Restriction:
Letter Grade or Pass/Fail
Contact:
Ayesha Khan
Course Instructor:
  • Steve Selvin
Resources:
Description:

Presents applications of regression techniques , starting with a review of simple linear regression, as a foundation. Followed by application to non-linear data using more general regression techniques. Then, a complete and extensive description of log-linear regression analysis (also called Poisson regression) and how it works, particularly for the application to count data and tables. Also included is the concept of quasi-independence and the analysis of incomplete tables. Logistic regression techniques are similarly described in detail with emphasis on application to epidemiologic binary outcome data in several contexts. All regression techniques are illustrated

with applied examples.

Learning Objectives:

Upon successfully completing this course, students will be able to:

  1. Understand and apply regression multivariable statistical tools to analyze multivariate data
  2. Effective use of log-linear/Poisson regression methods in the context of count data and analysis of tables
  3. Perform logistic regression techniques applied to binary outcomes generated from multivariate data
Methods of Assessment:

Final exam or paper

Instructor Consent:

No consent required